Compositional distributional semantics in natural language processing allows us to compose meaning vectors for complex phrases from those for word vectors. Here we apply the framework of compositional distributional semantics to construct meaning vectors for bank teller and feminist bank teller in the well-known Linda problem, and compare them via cosine similarity to a meaning vector encoding the Linda description provided in the problem. In a noun vector space, the meaning vector for the conjoined phrase feminist bank teller is substantially closer to the meaning vector for the Linda description than the meaning vector for the occupation-only phrase bank teller; crucially, this effect requires the full adjective-as-matrix treatment provided by compositional distributional semantics. In this way, we can provide a computational linguistic account of the conjunction effect in the Linda problem.